Female athletic participation and income: evidence from a latent class model

Steven B Caudill, James E. Long and Franklin Mixon

Journal of Applied Statistics, 2012, vol. 39, issue 3, 477-488

Abstract: This paper introduces and applies an EM algorithm for the maximum-likelihood estimation of a latent class version of the grouped-data regression model. This new model is applied to examine the effects of college athletic participation of females on incomes. No evidence for an “athlete” effect in the case of females has been found in the previous work by Long and Caudill [12], Henderson et al. [10], and Caudill and Long [5]. Our study is the first to find evidence of a lower wage for female athletes. This effect is present in a regime characterizing 42% of the sample. Further analysis indicates that female athletes in many otherwise low-paying jobs actually get paid less than non-athletes.

Date: 2012
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DOI: 10.1080/02664763.2011.596194

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